Aggregated Learning
2000년 도입 · 논문 1편에서 사용
Aggregated Learning (AgrLearn) is a vector-quantization approach to learning neural network classifiers. It builds on an equivalence between IB learning and IB quantization and exploits the power of vector quantization, which is well known in information theory.
출처: Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
소개 논문: Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
Information Bottleneck · General